Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge
A key bottleneck for developing dialog models is the lack of adequate training data. Due to privacy issues, dialog data is even scarcer in the health domain. We propose a novel method for creating dialog corpora which we apply to create doctor-patient interaction data. We use this data to learn both a generation and a hybrid classification/retrieval model and find that the generation model consistently outperforms the hybrid model. We show that our data creation method has several advantages. Not only does it allow for the semi-automatic creation of large quantities of training data. It also provides a natural way of guiding learning and a novel method for assessing the quality of human-machine interactions.
Code (0)
등록된 구현이 없습니다.
Tasks
RetrievalSimilar Papers 제목 키워드 기반
Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools
Objective: This study aims to develop and validate an evaluation framework to ensure the safety and reliability of mental health chatbots, which are increasingly popular due to their accessibility, human-like interaction…
ChatbotLarge Language ModelDiscovering Textual Structures: Generative Grammar Induction using Template Trees
Natural language generation provides designers with methods for automatically generating text, e.g. for creating summaries, chatbots and game content. In practise, text generators are often either learned and hard to int…
Text GenerationPosting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques
Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curator…
ArticlesBIG-bench Machine LearningClusteringMental Health Assessment for the Chatbots
Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics. For a chatbot which …
ChatbotMental Health Assessment for the Chatbots
Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics. For a chatbot which …
Chatbot